Interpretable machine learning models (imodels) 🔍

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily understandable. Pull requests very welcome!

Github • Popular imodels • Custom imodels • Demo notebooks

Implementations of different interpretable models, all compatible with scikit-learn. The interpretable models can be easily used and installed:

from imodels import BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier
from imodels import SLIMRegressor, RuleFitRegressor

model = BayesianRuleListClassifier()  # initialize a model
model.fit(X_train, y_train)   # fit model
preds = model.predict(X_test) # discrete predictions: shape is (n_test, 1)
preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n_test, n_classes)

Install with pip install imodels (see here for help). Contains the following models:

Model Reference Description
Rulefit 🗂️, 🔗, 📄 Extracts rules from a decision tree then builds a sparse linear model with them
Skope rules 🗂️, 🔗 Extracts rules from gradient-boosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision
Bayesian rule list 🗂️, 🔗, 📄 Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic)
Greedy rule list 🗂️, 🔗 Uses CART to learn a list (only a single path), rather than a decision tree
Iterative random forest 🗂️, 🔗, 📄 (In progress) Repeatedly fit random forest, giving features with high importance a higher chance of being selected.
Optimal classification tree 🗂️, 🔗, 📄 (In progress) Learns succinct trees using global optimization rather than greedy heuristics
Sparse integer linear model 🗂️, 📄 Forces coefficients to be integers
Rule sets (Coming soon) Many popular rule sets including SLIPPER, Lightweight Rule Induction, MLRules

Docs 🗂️, Reference code implementation 🔗, Research paper 📄

Custom interpretable models

The code here contains many useful and readable functions for a variety of rule-based models, contained in the util folder. This includes functions and simple classes for rule deduplication, rule screening, converting between trees, rulesets, and pytorch neural nets. The final derived rules easily allows for extending any of the following general classes of models:

Rule set Rule list (Decision) Rule tree Algebraic models

Demo notebooks

Demos are contained in the notebooks folder.

  • model_based.ipynb, demos the imodels package. It shows how to fit, predict, and visualize with different interpretable models
  • this notebook shows an example of using imodels for deriving a clinical decision rule
  • we also include some demos of posthoc analysis, which occurs after fitting models
  • posthoc.ipynb - shows different simple analyses to interpret a trained model
  • uncertainty.ipynb - basic code to get uncertainty estimates for a model

References

For updates, star the repo, see this related repo, or follow @csinva_. Please make sure to give authors of original methods / base implementations appropriate credit!

Expand source code 
"""
.. include:: ../readme.md
"""
# Python `imodels` package for interpretable models compatible with scikit-learn.
# Github repo available [here](https://github.com/csinva/interpretability-implementations-demos).

from .rule_list.bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
from .rule_list.greedy_rule_list import GreedyRuleListClassifier
from .rule_set.rule_fit import RuleFitRegressor
from .rule_set.skope_rules import SkopeRulesClassifier
# from .tree.iterative_random_forest.iterative_random_forest import IRFClassifier
# from .tree.optimal_classification_tree import OptimalTreeModel
from .algebraic.slim import SLIMRegressor

CLASSIFIERS = BayesianRuleListClassifier, GreedyRuleListClassifier #, IRFClassifier
REGRESSORS = RuleFitRegressor, SkopeRulesClassifier, SLIMRegressor

Sub-modules

algebraic
rule_list
rule_set
tree
util

Shared utilities for implementing different interpretable models.